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The availability of free, high-resolution satellite imagery and advanced data mining techniques offers new opportunities for efficient soil property assessment. This study aimed to evaluate the potential utility of multi-season PlanetScope imagery to predict soil organic carbon (SOC), pH, and calcium carbonate (CaCO<jats:sub>3<\/jats:sub>). Using random sampling, 121 topsoil samples (0\u201330\u00a0cm depth) were collected with an auger across grasslands, bare soil, and eroded areas within a typical grazing land use. Three data mining techniques: random forest (RF), extreme gradient boosting (XGB), and support vector machines (SVM), were applied and evaluated using a 10-fold cross-validation. The results indicated that multi-season spectral covariates considerably improved the accuracy of the target soil properties compared to single-season imagery. SVM was the most effective algorithm for predicting SOC, achieving a root mean square error (RMSE) of 0.52%, mean absolute error (MAE) of 0.24%, and R\u00b2 of 0.92. RF was the best-performing algorithm for predicting soil pH (RMSE\u2009=\u20090.22, MAE\u2009=\u20090.17, and R\u00b2 = 0.97) and CaCO<jats:sub>3<\/jats:sub> (RMSE\u2009=\u20090.55%, MAE\u2009=\u20090.42%, and R\u00b2 = 0.96). While XGB failed to capture the variability in soil pH, the other models generated interpretable maps that accurately represented the distribution of soil properties across different land cover categories. The green-red vegetation index (GRVI) was the most critical covariate for predicting SOC, while elevation and the topographic wetness index (TWI) were key predictors for soil pH and CaCO<jats:sub>3<\/jats:sub>, respectively. This study underscores the potential of multi-season PlanetScope imagery for accurately predicting soil properties and recommends conducting similar studies in diverse geographical settings to validate these findings and develop more generalizable models.<\/jats:p>","DOI":"10.1007\/s12145-025-01748-6","type":"journal-article","created":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T03:42:41Z","timestamp":1738381361000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Improving the spatial prediction of topsoil properties in a typical grazing area using multi-season PlanetScope spectral covariates and data mining techniques"],"prefix":"10.1007","volume":"18","author":[{"given":"Kwanele","family":"Phinzi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"L\u00e1szl\u00f3","family":"Bertalan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gashaw Gismu","family":"Chakilu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Szil\u00e1rd","family":"Szab\u00f3","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,1]]},"reference":[{"key":"1748_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/15481603.2019.1650447","volume":"57","author":"AM Abdi","year":"2020","unstructured":"Abdi AM (2020) Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data. 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